Cong Fu
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Cong Fu, Ph.D. student |
Short Biography
I am currently a fourth-year Ph.D. student in Computer Science at Texas A&M University. My advisor is Shuiwang Ji, who leads the Data Integration, Visualization, and Exploration (DIVE) Laboratory.
Research Interests
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Machine Learning
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Graph & Geometric Deep Learning
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AI for Science
News
01/2024 Our paper SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations has been accepted to ICLR 2024.
01/2024 Our paper Complete and Efficient Graph Transformers for Crystal Material Property Prediction has been accepted to ICLR 2024.
12/2023 Our paper Lattice Convolutional Networks for Learning Ground States of Quantum Many-body Systems has been accepted to SDM 2023.
11/2023 Our paper A Latent Diffusion Model for Protein Structure Generation has been accepted to LoG 2023.
11/2023 Our paper Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction has been accepted to LoG 2023.
11/2023 Check out our AI4Science survey paper Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems! [Github] [Website]
04/2023 Our paper Group Equivariant Fourier Neural Operators for Partial Differential Equations has been accepted to ICML 2023.
09/2021 Our paper DIG: A Turnkey Library for Diving into Graph Deep Learning Research has been accepted to JMLR.
06/17/2021 Our team DIVE@TAMU is an awardee of KDD Cup 2021 on OGB-LSC! [report] [code] [TAMU News]
03/2021 Our libray DIG: Dive into Graphs is released! DIG is a research-oriented library that includes unified and extensible implementations of algorithms for graph generation, self-supervised learning on graphs, explainability of graph neural networks, and deep learning on 3D graphs.
Education
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Ph.D., Computer Science, Texas A&M University, Spring 2021 - Present
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M.S., Mechanical Engineering, University of Michigan -- Ann Arbor, Fall 2016 - Spring 2018
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B.S., Mechanical Engineering, Harbin Institute of Technology, September 2012 - July 2016
Experience
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Research Intern, Fujitsu Research of America, Sunnyvale, CA, July 2022 - December 2022 (Supervised by Kanji Uchino)
Work on developing SE(3) latent diffusion model for protein backbone generation -
Robotics Engineer, DMAI, Inc., Los Angeles, CA, June 2018 - March 2020 (Supervised by Hangxin Liu and Yixin Zhu)
Work on developing mobile robot autonomous navigation systems and building bipedal robotics system
Preprints
Xuan Zhang*, Limei Wang*, Jacob Helwig*, Youzhi Luo*, Cong Fu*, Yaochen Xie*, Meng Liu, Yuchao Lin, ..., Shuiwang Ji (63 authors)
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
arXiv:2307.08423, 2023
[Paper] [Code] [Website]
Publications
Keqiang Yan, Cong Fu, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji
Complete and Efficient Graph Transformers for Crystal Material Property Prediction
International Conference on Learning Representations (ICLR), 2024
[Paper] [Code]Xuan Zhang, Jacob Helwig, Yuchao Lin, Yaochen Xie, Cong Fu, Stephan Wojtowytsch, Shuiwang Ji
SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations
International Conference on Learning Representations (ICLR), 2024
[Paper] [Code]Cong Fu*, Keqiang Yan*, Limei Wang, Wing Yee Au, Michael McThrow, Tao Komikado, Koji Maruhashi, Kanji Uchino, Xiaoning Qian, Shuiwang Ji
A Latent Diffusion Model for Protein Structure Generation
Learning on Graphs Conference (LoG), 2023
[Paper] [Code]Cong Fu, Jacob Helwig, Shuiwang Ji
Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction
Learning on Graphs Conference (LoG), 2023
[Paper] [Code]Cong Fu*, Xuan Zhang*, Huixin Zhang, Hongyi Ling, Shenglong Xu, Shuiwang Ji
Lattice Convolutional Networks for Learning Ground States of Quantum Many-body Systems
SIAM International Conference on Data Mining (SDM), 2023
[Paper] [Code]Jacob Helwig*, Xuan Zhang*, Cong Fu, Jerry Kurtin, Stephan Wojtowytsch, Shuiwang Ji
Group Equivariant Fourier Neural Operators for Partial Differential Equations
International Conference on Machine Learning (ICML), 2023
[Paper] [Code]Meng Liu*, Cong Fu*, Xuan Zhang, Limei Wang, Yaochen Xie, Hao Yuan, Youzhi Luo, Zhao Xu, Shenglong Xu, Shuiwang Ji
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
AI for Science Workshop at NeurIPS, 2021
Runner-up award of KDD Cup 2021 on OGB-LSC!
[Paper] [Code]Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, Shuiwang Ji
DIG: A Turnkey Library for Diving into Graph Deep Learning Research.
Journal of Machine Learning Research (JMLR), 2021
[Paper] [Code]
Services
Conference Program Committee Member & Reviewer
International Conference on Machine Learning (ICML), 2022, 2023
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Annual Conference on Neural Information Processing Systems (NeurIPS), 2022, 2023
NeurIPS Track Datasets and Benchmarks, 2023
International Conference on Learning Representations (ICLR), 2023
Conference on Information and Knowledge Management (CIKM), 2023
Structured Probabilistic Inference & Generative Modeling Workshop @ ICML, 2023
New Frontiers of AI for Drug Discovery and Development Workshop @ NeurIPS, 2023
Generative AI and Biology Workshop @ NeurIPS, 2023
AI4Science Workshop @ NeurIPS, 2022, 2023
Journal Reviewer
ACM Transactions on Knowledge Discovery from Data (TKDD), 2023
Awards
Travel Grant, CSE@TAMU, 2023
Travel Award, AI4Science Workshop @ NeurIPS, 2021
Runner-up Award, KDD Cup on Open Graph Benchmark Large-Scale Challenge (OGB-LSC), 2021
National Scholarship, China, 2014
Teaching Assistant
CSCE 421 Machine Learning, Texas A&M University, 2023